A causal inference study on the effects of first year workload on the dropout rate of undergraduates

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  • dc.contributor.author Karimi-Haghighi, Marzieh
  • dc.contributor.author Castillo, Carlos
  • dc.contributor.author Hernández Leo, Davinia
  • dc.date.accessioned 2023-02-14T07:06:25Z
  • dc.date.available 2023-02-14T07:06:25Z
  • dc.date.issued 2022
  • dc.description Comunicació presentada a 23rd International Conference on Artificial Intelligence in Education (AIED 2022), celebrat del 27 al 31 de juliol de 2022 a Durham, Regne Unit.
  • dc.description.abstract In this work, we evaluate the risk of early dropout in undergraduate studies using causal inference methods, and focusing on groups of students who have a relatively higher dropout risk. We use a large dataset consisting of undergraduates admitted to multiple study programs at eight faculties/schools of our university. Using data available at enrollment time, we develop Machine Learning (ML) methods to predict university dropout and underperformance, which show an AUC of 0.70 and 0.74 for each risk respectively. Among important drivers of dropout over which the first-year students have some control, we find that first year workload (i.e., the number of credits taken) is a key one, and we mainly focus on it. We determine the effect of taking a relatively lighter workload in the first year on dropout risk using causal inference methods: Propensity Score Matching (PSM), Inverse Propensity score Weighting (IPW), Augmented Inverse Propensity Weighted (AIPW), and Doubly Robust Orthogonal Random Forest (DROrthoForest). Our results show that a reduction in workload reduces dropout risk.
  • dc.description.sponsorship This work has been partially supported by: the HUMAINT programme (Human Behaviour and Machine Intelligence), Joint Research Centre, European Commission; “la Caixa” Foundation (ID 100010434), under the agreement LCF/PR/PR16/51110009; and the EU-funded “SoBigData++” project, under Grant Agreement 871042. In addition, D. Hernández-Leo acknowledges the support by ICREA under the ICREA Academia programme, and the National Research Agency of the Spanish Ministry (PID2020-112584RB-C33/MICIN/AEI/10.13039/501100011033).
  • dc.format.mimetype application/pdf
  • dc.identifier.citation Karimi-Haghighi M, Castillo C, Hernández-Leo D. A causal inference study on the effects of first year workload on the dropout rate of undergraduates. In: Mercedes Rodrigo M, Matsuda N, Cristea AI, Dimitrova V, editors. Artificial Intelligence in Education. 23rd International Conference, AIED 2022; 2022 Jul 27-31; Durham, United Kingdom. Cham: Springer; 2022. p. 15-27. DOI: 10.1007/978-3-031-11644-5_2
  • dc.identifier.doi http://dx.doi.org/10.1007/978-3-031-11644-5_2
  • dc.identifier.issn 0302-9743
  • dc.identifier.uri http://hdl.handle.net/10230/55757
  • dc.language.iso eng
  • dc.publisher Springer
  • dc.relation.ispartof Mercedes Rodrigo M, Matsuda N, Cristea AI, Dimitrova V, editors. Artificial Intelligence in Education. 23rd International Conference, AIED 2022; 2022 Jul 27-31; Durham, United Kingdom. Cham: Springer; 2022. p. 15-27.
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/871042
  • dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/PID2020-112584RB-C33
  • dc.rights © Springer This is a author's accepted manuscript of: Karimi-Haghighi M, Castillo C, Hernández-Leo D. A causal inference study on the effects of first year workload on the dropout rate of undergraduates. In: Mercedes Rodrigo M, Matsuda N, Cristea AI, Dimitrova V, editors. Artificial Intelligence in Education. 23rd International Conference, AIED 2022; 2022 Jul 27-31; Durham, United Kingdom. Cham: Springer; 2022. p. 15-27. The final version is available online at: http://dx.doi.org/10.1007/978-3-031-11644-5_2
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.subject.keyword University dropout
  • dc.subject.keyword Machine learning
  • dc.subject.keyword Causal inference
  • dc.subject.keyword Average treatment effect
  • dc.title A causal inference study on the effects of first year workload on the dropout rate of undergraduates
  • dc.type info:eu-repo/semantics/conferenceObject
  • dc.type.version info:eu-repo/semantics/acceptedVersion

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